Evidence map›Paper›PMID 39407257›Full record

ArticleClinical epigenetics2024

Influence of physical activity on the epigenetic clock: evidence from a Japanese cross-sectional study.

Masatoshi Nagata, Shohei Komaki, Yuichiro Nishida, Hideki Ohmomo, Megumi Hara, Keitaro Tanaka, Atsushi Shimizu

Abstract read
In one paragraph

Article in Clinical epigenetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Masatoshi NagataDivision of Biomedical Information Analysis, Institute for Biomedical Sciences of Iwate Medical University, 1-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3694, Japan.
Shohei KomakiDivision of Biomedical Information Analysis, Institute for Biomedical Sciences of Iwate Medical University, 1-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3694, Japan.
Yuichiro NishidaDepartment of Preventive Medicine, Faculty of Medicine, Saga University, Saga, Japan.
Hideki OhmomoDivision of Biomedical Information Analysis, Institute for Biomedical Sciences of Iwate Medical University, 1-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3694, Japan.
Megumi HaraDepartment of Preventive Medicine, Faculty of Medicine, Saga University, Saga, Japan.
Keitaro TanakaDepartment of Preventive Medicine, Faculty of Medicine, Saga University, Saga, Japan.
Atsushi ShimizuDivision of Biomedical Information Analysis, Institute for Biomedical Sciences of Iwate Medical University, 1-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3694, Japan. ashimizu@iwate-med.ac.jp.

Funding

Japan Society for the Promotion of Science 17015018, 221S0001, 18390182, 20249038, 16H06277, 17H01554, 22H03468, and 22H04923 [CoBiA]
6 · The paper itself

Abstract

backgroundBiological age, especially epigenetic age derived from the epigenetic clock, is a significant measure of aging, considering the differences in aging rates among individuals. The epigenetic clock, a machine learning-based algorithm, uses DNA methylation states to estimate biological age. Previous studies have reported inconsistent associations between physical activity (PA) and the epigenetic clock, especially second-generation clocks such as PhenoAge and GrimAge. This study aimed to clarify this relationship using cross-sectional data from Japanese participants aged 40-69.

methodsWe used two datasets from the Saga J-MICC study, of which 867 samples were available for analysis. DNA methylation data from peripheral blood samples were used to calculate the epigenetic age using the epigenetic clocks PhenoAge and GrimAge. PA and sedentary time were measured using a single-axis accelerometer, while self-reported PA, sedentary time, and covariates were assessed using a self-administered questionnaire. The association between PA or sedentary time and epigenetic age acceleration was assessed using multiple linear regression.

resultsPearson's correlation coefficients between accelerometer-based and self-reported PA variables ranged from 0.09 to 0.20. Multivariable regression analysis showed that accelerometer-based PA and sedentary time were associated with epigenetic age decelerations and accelerations, respectively. However, self-reported PA was not associated with the epigenetic age accelerations.

conclusionsThese results indicate that reducing sedentary time and increasing PA were associated with slowing both PhenoAge and GrimAge, even in East Asian populations with different exercise habits, body shapes, and lifestyles. This study highlights the potential of objective second-generation epigenetic age acceleration as an outcome index for healthcare interventions and clinical applications.

Indexed as

AgingDNA MethylationEpigenesis, GeneticExerciseAccelerometryAdultAgedCross-Sectional StudiesEast Asian PeopleFemaleHumansJapanMachine LearningMaleMiddle AgedSedentary BehaviorAccelerometer-based measurementAge accelerationEpigenetic clockPhysical activitySedentary

Identifiers

PMID39407257
PMCPMC11481432

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.